无线传感器网络定位技术研究

节点定位技术是传感器网络关键技术之一,具有十分重要的地位.传感器节点采集到的数据必须结合其位置信息才有意义,没有位置信息的数据几乎没有利用价值.叙述了一些典型定位算法,并将这些定位算法归纳为两大类:Range-based的和Range-free的定位算法.最后,将两类定位算法结合起来提出了值得进一步研究的问题.     

无线传感器网络的节点定位方法

提出了一种MCBN (Monte Carlo localization boxed using non-anchor)定位算法.该算法建立在蒙特卡罗定位算法基础之上,利用两跳范围内可信任度权值最小且坐标确定的静态非锚节点,辅助网络中两跳范围内的锚节点构建最小锚盒,同时利用待定位节点上一时刻的位置信息和临时锚节点的特性增强样本过滤条件,进行快速抽样和样本过滤.仿真结果表明:MCBN同MCL和MCB算法相比,提高了节点定位精度,降低了节点能量损耗.     

信号强度和运动向量结合的无线传感器网络移动节点定位

无线传感器网络的定位是近年来无线传感器网络研究的重要课题.本文首先介绍了无线传感器网络的来源、重要性以及无线传感器网络定位的分类.然后提出了一种全新定位算法,信号强度和运动向量结合的无线传感器网络移动节点定位,简称SSMV算法,在外围布置四个锚节点,得用信号强度和未知节点在运动中向量的变化,对锚节点在内的未知节点进行定位,并对该算法进行了仿真和总结.通过与凸规划法进行比较,仿真结果表明,该算法有更高的定位精度.     

基于贪婪思想的二阶段无线传感器网络定位算法

近些年来,将优化算法应用到节点定位问题当中成为了一个研究热点.本文假设下一次定位结果为准确坐标,对前后两次定位结果邻居节点之间距离关系进行深度分析和推导,得到一个邻域函数.在此基础上根据贪婪思想,提出了贪婪定位算法.为了达到更精确的定位结果,本文将贪婪定位算法分成两个阶段:第一阶段,根据贪婪迭代优化得到一组初始定位结果;第二阶段将满足一定条件的未知节点升级为锚节点,重新执行第一阶段的过程,重复第二阶段,直到没有未知节点可以升级为锚节点为止.实验结果表明,无论是定位精确度还是算法执行时间,本文所提算法都比当前的一些优化定位算法要好.     

无线传感器网络中Robust Position定位算法的改进

首先深入分析了两种典型的分布式的节点定位算法，并从定位精度及能量消耗两个方面给出了定量分析结果。在此基础上提出了改进的定位算法，BB_RP算法。通过仿真试验，在定位精度和能量消耗两个方面与原有算法进行定量的分析比较。结果表明，BB_RP算法虽然在定位精度略低于原有的算法，但是在能量消耗和覆盖速度两个指标上有了较大的提高，使得无线传感器网络的生命周期延长。     

改进的DV-Hop无线传感网络节点定位算法

针对传感器部署密度大、分布不均匀,DV-Hop定位算法误差大等问题,提出了一种改进DV-Hop的无线传感器节点定位算法.首先采用DV-Hop算法对未知传感器节点位置进行计算,然后在采用遗传算法对DV-Hop定位的误差进行修正.仿真结果表明,改进DV-Hop算法提高了节点的定位精度,降低定位的误差,更能真实地反映传感器网络节点的实际分布情况.     

无线传感器网络中一种基于定向天线的节点定位算法

节点定位问题是无线传感器网络中的一个基本而非常重要的问题。已有的定位算法大致可以分为两类:基于测距的和无需测距的。基于测距的定位算法需要额外硬件的支持,成本较高,不适用于大规模的传感器网络;无需测距的定位算法不需要额外硬件的支持,是解决传感器网络节点定位问题的一个较为经济适用的方法。本文提出了一种新的基于定向天线的无需测距的分布式定位算法。其主要原理在于利用定向天线确定邻居节点之间的相对位置,然后判断未知节点是否在锚节点构成的三角形内。仿真研究表明,提出的算法是有效的,能够取得较好的定位精度。     

基于临近锚节点修正的DBSCAN聚类加权定位算法

为了提高无线传感器网络节点的定位精确度,给出一种基于临近锚节点修正(CAAN)的具有噪声的基于密度的聚类(DBSCAN)加权定位算法。首先,在未知节点通信范围内的锚节点中选择三个构成三角形,证明当未知节点处在此三角形外接圆圆心位置时定位误差最小,然后据此选择合适的锚节点,结合滤波后的接收信号强度指示(RSSI)值进行定位计算,并利用DBSCAN聚类算法剔除误差较大的值。其次,把聚类后所得簇的核心点个数当作权值,采用加权定位算法得到未知节点的初始坐标。最后,计算锚节点坐标与初始坐标间的距离,选择临近的锚节点修正初始坐标,使最终的定位结果更加精确。仿真结果表明：相比于加权质心定位算法和基于RSSI测距滤波优化的加权质心定位算法,所给算法的定位精确度分别提高了69.55%和38.64%。     

无线传感器网络节点定位技术

本文从算法性能、已有的定位算法、定位算法的分类和WSN节点定位机制的评价标准等方面,针对无线传感器的网络定位技术进行详细的分析和探讨。并与经典的APIT算法、凸规划定位算法进行了比较。结论显示出在不同的环境之下不同的算法各有优劣,并不能指出最好的算法。实际操作中应该根据实际的需求来选择算法,并对未来无限传感器网络节点定位技术的发展方向做出了预测。     

基于接收信号强度的WSN概率定位算法

定位对无线传感器网络的应用、操作和管理发挥着至关重要的作用.针对传感器节点的定位,提出了一种基于接收信号强度的概率定位算法.介绍了算法原理及实现过程,讨论了信标节点分布对该算法性能的影响,最后比较了本概率定位算法和最小二乘定位算法在传感器节点定位性能上的优劣.仿真结果表明,信标节点分布对未知节点的定位误差具有较大的影响,本定位算法的性能要优于最小二乘定位算法.     

室内定位技术及应用综述

室内定位技术作为当前的研究热点,对军事、经济以及人们的日常生活产生了深远的影响。文中首先介绍了室内定位技术的应用背景以及发展方向,以及室内环境进行定位面临的主要难点。然后介绍了目前主要的定位算法,包括邻近信息、场景分析和几何定位算法,以及各种算法衍生出来的各种定位技术。最后指出了室内定位技术亟待解决的问题,以及下一步进行相关研究的突破口。     

Localization algorithms of Wireless Sensor Networks: a survey

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.     

移动无线传感器网络中节点自定位算法研究

移动无线传感器网络(MWSN)是一种全新的信息获取和处理技术,可以在广泛的应用领域内实现复杂的大规模监测和追踪任务,而移动节点的位置信息则是其应用的基础。针对这一特征,文章深入探讨了移动节点自定位算法的研究现状,介绍了移动无线传感器网络节点定位的基本方法,讨论了定位算法的性能评价指标,并对各种算法性能进行了比较,最后指出了算法存在的问题和解决办法。文章认为三维全节点移动的定位算法将成为以后的研究趋势。     

基于仿射传播聚类的概率分布室内定位算法

随着定位技术的快速发展,基于无线局域网的室内定位成为新的研究热点。本文提出了一种基于近邻传播聚类的概率分布无线局域网(WLAN)室内定位算法。与传统室内定位算法相比,该算法首先引入近邻传播聚类缩小参考点搜索空间,然后利用概率分布定位算法进行精确定位。仿射传播聚类可以有效减少概率分布定位算法的计算量,应用于系统后将有效降低系统复杂度。实验结果表明,本文所提算法具有更好的定位精度,可实现对WLAN室内定位目标的快速、可靠定位。     

A survey on deployment techniques,localization algorithms,and research challenges for underwater acoustic sensor networks

In recent years, wireless sensor networks (WSNs) have attracted the attention of both the research community and the industry, and this has eventually lead to the widespread use of WSNs in various applications. The significant advancements in WSNs and the advantages brought by WSNs have also enabled the rapid development of underwater acoustic sensor networks (UASNs). In UASNs, in addition to deployment, determining the locations of underwater sensor nodes after they have been deployed is important since it plays a critical role in many applications. Various localization techniques have been proposed for UASNs, and each one is suitable for specific scenarios and has unique challenges. In this paper, after presenting an overview of potential UASN applications, a survey of the deployment techniques and localization algorithms for UASNs has been presented based on their major advantages and disadvantages. Finally, research challenges and open research issues of UASNs have been discussed to provide an insight into future research opportunities.     

Underwater localization using stochastic proximity embedding and multi-dimensional scaling

Localization for underwater acoustic sensor networks is an active research topic where a large number of techniques have been proposed recently. This paper addresses one of the open research issues, the impact of underwater sound speed variation on the localization accuracy. In this paper, modified versions of stochastic proximity embedding and multi-dimensional scaling localization algorithms customized for underwater application are proposed. The algorithms are found to provide good performance in underwater scenario as they take into account refractive ray bending of acoustic waves. Detailed study of the algorithm performance has been done and the results are reported. Cramer Rao Lower Bound for the problem is also derived.     

Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks

The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.     

无线传感器网络的节点自定位技术

文章对无线传感器网络的节点定位机制与算法进行了介绍,并对基于测距的和不基于测距的两大类方法进行了分析对比.文章认为节点定位是无线传感器网络的一项关键技术,对于无线传感器网络的许多应用来说节点位置信息都是必须的基本信息,虽然目前已有不少节点定位技术,但仅仅是一些初步的研究成果,距离无线传感器网络的整体优化目标还很不够,需要继续深入研究开发,提出更多的高效算法,促进无线传感器网络进一步的普及应用.     

基于小波变换的嵌入式图像编码算法的综述

小波变换所具有的良好的空间一频率局部化特性给基于此的图像编码算法提供了广阔的发展空间。其中嵌入式图像编码算法因其提供的良好特性成为静止图像编码新标准JPEG2000的基础。本文对基于小波变换的嵌入式图像编码方法近十年来的研究进行了较全面的综述,着重分析了几种典型算法的原理、实现和编码性能,并探讨了此方法可能的发展方向。     

Theoretic analysis of unique localization for wireless sensor networks

Node self-localization has become an essential requirement for realistic applications over wireless sensor networks (WSNs). Although many distributed localization algorithms have been proposed, fundamental theoretic analysis of unique localization is still in its early stage of development. This paper aims at a synthetic and homogeneous survey of the theoretical basis on WSN localization problem carried out thus far. Specifically, subsequent to establishing a technological context of relevant terms, we construct a graph and then a formation for each WSN to present current state-of-the-art by analyzing possible conditions for unique localization, as well as corresponding verification algorithms, by drawing on the powerful results from rigidity theory, distance geometry, geometric constraints in CAD, and combinatorial theory. We show that the unique localization problem is well understood in two-dimension, however, only partial analogous results are available in three-dimension.